Evaluating Impacts of Climate Change on Traditional Mexican Maize

Steven C. Gonzalez
December 6th, 2017

Presented to:
Dr. Russell Weaver (Chair)
Dr. Jennifer Jensen
Dr. Thomas Ballinger

M.S. Thesis Proposal
Department of Geography
Texas State University, San Marcos, TX
github: www.github.com/stevenconnorg/maices-enm
contact: scg67@txstate.edu

Introduction


  • Climate change is expected heavily impact natural and human systems worldwide (Walther, G.-R. et al., 2002; IPCC 2007, 2013; Kang, Y, S. Khan 2009; Hoegh-Guldberg, O. 2010)
  • The IPCC reports numerous negative impacts of climate change on domesticated crops regardless of the emission scenario implemented(Brown and Funk 2008; Ureta et al. 2012; IPCC 2013)
  • Ensuring global food security in an increasingly warming world with exponentially increasing world population requires multidisciplinary research (Brown and Funk 2008; Ureta et al. 2012; IPCC 2013, 2015)





Five percent reduction in crop season, sensitivity to change, capacity to cope. Source: CGIAR.

Mexico: Maize's 'C.O.D.'


  • Maize (Zea mays subsp. mays L.) supplies a staple food crop for more than 200 million people worldwide (Nuss and Tanumihardjo 2010; Ureta et al. 2013)
  • Climate change is expected to alter maize suitability, especially between the tropics (Ramirez-Cabral et al., 2017)
  • Mexico, maize's C.O.D., harbors traditional maize landraces that account for 60% of maize genetic diversity globally (Ureta et al. 2013)
  • Preservation of agrobiodiversity at centers of domestication critical for global food security (Thrupp 2000; Esquinas-Alcazar 2005; Ureta et al. 2013).


Source: The International Maize and Wheat Improvement Center (CIMMYT)

Maize Diversity & Evolution


  • > 9,000 years from Mexican annual teosinte (Z. mays ssp. Parviglumus and ssp. mexicana ) in highlands via artificial selection by indigenous cultures (Matsuoka et al. 2002; Kato et al. 2009)
  • Maize landraces have been grown (Ruiz Corral et al. 2008):
    • from sea-level to 2,900 (m asl)
    • avg. growing temp 12.0 °C - 29.1 °C
    • avg. seasonal precip. 400 mm to 3555 mm
  • Offer valuable genetic resources

Maize Mirrors Humanity


  • As a domesticated crop, maize is intimately contingent upon societal influences:
    • Seed management practices (Anderson 1947; Dyer and Lopez-Feldman 2013)
    • Indigenous religious practices (R. Ortega-Paczka., 2003)
    • Ethnolinguistic diversity (Perales, Benz, and Brush 2005; Brush and Perales 2007; Rivero-Romero et al. 2016)
    • Indigenous agricultural knowledge (Garcia-Martinez et al. 2016; Rivero-Romero et al. 2016)
    • Agro-technological practices (Garcia-Martinez et al. 2016; Rivero-Romero et al. 2016; Toledo and Barrera-Bassols 2017)
    • variations across ethnic groups (Perales, Benz, and Brush 2005; Brush and Perales 2007)





“Maize is a sensitive mirror of the people who grow it” (1942)



“Mexico, more than any other country in the New World, is the land of corn” (1946)



- Edgar Anderson (1897-1969)
American researcher of corn

Ethno-linguistic Diversity of Mexico


  • La Comision Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO)
  • 2000 indigenous population % by municipio (n = 2446 + 16)
  • 68 indigenous macro-languages in Mexico (Instituto Nacional de Lenguas Indigenas (INALI))
    • In reality, many more distinct languages and dialects
    • 10-14% identify as indigenous, but only 6% speak an indigenous language
    • Stability is dubious
    • Loss of potentially irrecoverable deep climatic knowledge


plot of chunk pobInd

1st - 4th major indigenous languages by municipality (1990)

LENGUA1 LENGUA2 LENGUA3 LENGUA4
0 MIXTECO ZAPOTECO NA NA
1 PUREPECHA MIXTECO NA NA
2 MIXTECO PUREPECHA NA NA
3 CUCAPA PUREPECHA NA NA
5 NAHUATL MIXTECO YAQUI NA
6 PIMA ALTO MAYO NA NA
7 TARAHUMARA YAQUI PIMA NA
8 TARAHUMARA MAZAHUA NA NA
9 MIXTECO ZAPOTECO MAYO NA
10 MAYO CHINANTECO MIXE OTOMI
11 PIMA ALTO MAYO TARAHUMARA NA
12 TARAHUMARA MAYA NA NA
13 MAYA NAHUATL NA NA
14 MIXTECO MAYO NA NA
15 TARAHUMARA ZAPOTECO NA NA
16 TARAHUMARA TEPEHUAN NA NA
17 PUREPECHA MAYO NA NA
18 YAQUI MAYO NA NA
19 TARAHUMARA YAQUI NA NA
20 PIMA ALTO NA NA NA
21 TARAHUMARA NA NA NA
22 MAYO YAQUI ZAPOTECO CHINANTECO
23 MAYO ZAPOTECO NA NA
25 NA NA NA NA
27 TARAHUMARA MAYO NA NA
28 SERI MAYO NA NA
31 TARAHUMARA PIMA NA NA
33 MAYO YAQUI NA NA
34 TARAHUMARA HUASTECO NA NA
35 MAYO NA NA NA
37 MAYO YAQUI CUCAPA NA
43 MAYA NA NA NA
44 NAHUATL NA NA NA
45 TARAHUMARA MAYO CHOL NA
46 AMUZGO NA NA NA
52 MAYO HUASTECO TARAHUMARA NA
53 PIMA TARAHUMARA NA NA
54 TOTONACA PUREPECHA MAYO NA
55 MAYO TARAHUMARA NA NA
58 NAHUATL ZAPOTECO NA NA
60 YAQUI NA NA NA
66 TARAHUMARA CAKCHIQUEL NA NA
67 TARAHUMARA MIXTECO NA NA
71 PIMA NAHUATL NA NA
72 TARAHUMARA PIMA OTOMI CHOCHO
74 MAYA PUREPECHA ZAPOTECO TARAHUMARA
76 HUASTECO NAHUATL MAZAHUA HUICHOL
80 TARAHUMARA PUREPECHA NA NA
82 HUASTECO NAHUATL NA NA
85 KIKAPU NAHUATL NA NA
86 YAQUI NAHUATL NA NA
87 TARAHUMARA MAYA TEPEHUAN NA
88 PIMA NA NA NA
89 OTOMI NAHUATL NA NA
90 TARAHUMARA TEPEHUAN PIMA NA
95 NAHUATL TOTONACA TARAHUMARA HUASTECO
96 ZAPOTECO HUASTECO NA NA
97 PIMA YAQUI TARAHUMARA MAYA
102 NAHUATL OTOMI NA NA
103 NAHUATL TOTONACA NA NA
104 TARAHUMARA NAHUATL NA NA
106 MAZAHUA YAQUI NA NA
107 OTOMI TARAHUMARA MAYA NA
110 MIXE ZAPOTECO NA NA
115 TARAHUMARA MIXTECO NAHUATL NA
125 TARAHUMARA HUICHOL NA NA
135 NAHUATL ZAPOTECO CHOL NA
139 TARAHUMARA HUAVE NA NA
144 TARAHUMARA TOTONACA ZAPOTECO DEL ISTMO MAYA
149 MAYO CAHITA NA NA
150 OTOMI NA NA NA
154 HUASTECO NA NA NA
160 TARAHUMARA YAQUI TEPEHUAN MAYA
163 TARAHUMARA MAZAHUA ZAPOTECO NA
164 TOTONACA NAHUATL NA NA
171 NAHUATL POPOLUCA NA NA
172 NAHUATL MAYA NA NA
175 NAHUATL TARAHUMARA MAYA ZAPOTECO
179 MAZAHUA ZAPOTECO NA NA
180 MAYA TARAHUMARA NA NA
187 MIXTECO MIXTECO DE LA MIXTECA ALTA NA NA
188 HUASTECO HUICHOL NAHUATL NA
190 TEPEHUAN TARAHUMARA NA NA
195 HUASTECO MAYA NA NA
202 HUASTECO TARAHUMARA NA NA
209 NAHUATL HUASTECO NA NA
216 NAHUATL TARAHUMARA NA NA
217 HUASTECO CHATINO NA NA
220 CHONTAL OTOMI NA NA
222 MIXTECO HUASTECO NA NA
223 CHONTAL HUASTECO TARAHUMARA CHOL
229 MAYA OTOMI NA NA
235 ZAPOTECO TEPEHUAN MIXTECO MAYO
236 TARAHUMARA MAYA NAHUATL OTOMI
242 NAHUATL MAYA TARAHUMARA NA
244 MAZAHUA TARAHUMARA NA NA
247 MAYO NAHUATL NA NA
248 NAHUATL TEPEHUAN MAYA NA
251 MAYA TOTONACA NAHUATL NA
255 MAYO MAZATECO NA NA
256 TOTONACA HUASTECO NA NA
257 CHICHIMECA JONAZ MAYA NA NA
258 ZAPOTECO MAYA NA NA
261 CHOL NAHUATL NA NA
263 TARAHUMARA NAHUATL MAYA NA
264 AGUACATECO NAHUATL NA NA
265 MAYA TARAHUMARA HUASTECO MIXTECO
270 CORA NAHUATL NA NA
272 TEPEHUAN TARAHUMARA ZAPOTECO MAYA
274 NAHUATL PUREPECHA HUICHOL NA
275 MIXTECO OTOMI HUICHOL NA
276 TARAHUMARA TEPEHUAN OTOMI NA
284 OTOMI TEPEHUAN NAHUATL NA
291 CHOL MIXTECO PUREPECHA NA
292 HUASTECO TEPEHUAN NA NA
293 TEPEHUAN OTOMI NA NA
295 MAZAHUA NA NA NA
299 NAHUATL HUICHOL MAYA NA
300 HUASTECO NAHUATL ZAPOTECO NA
303 TEPEHUAN NAHUATL NA NA
304 NAHUATL MAYA HUASTECO NA
305 TEPEHUAN MAYO MAYA HUICHOL
306 MIXTECO ZAPOTECO NAHUATL CORA
307 MIXTECO TARAHUMARA NA NA
309 NAHUATL TOTONACA HUASTECO MAYA
311 OTOMI PUREPECHA NAHUATL NA
313 HUAVE MAYA TEPEHUAN ZAPOTECO
314 ZAPOTECO MAYA MAZAHUA NAHUATL
316 CHONTAL DE OAXACA MIXTECO NA NA
317 HUASTECO OTOMI NA NA
322 ZAPOTECO NAHUATL NA NA
328 TEPEHUAN HUICHOL NA NA
329 CORA MAYO MIXTECO NAHUATL
331 TEPEHUAN TOTONACA NA NA
333 NAHUATL MIXTECO NA NA
336 HUICHOL MAZAHUA NA NA
338 NAHUATL PUREPECHA NA NA
343 TEPEHUAN MAYO NA NA
345 OTOMI PUREPECHA TOTONACA HUASTECO
348 OTOMI NAHUATL TARAHUMARA NA
349 CHONTAL DE OAXACA NAHUATL NA NA
350 NAHUATL CHICHIMECA JONAZ NA NA
356 TEPEHUAN CORA NA NA
357 AGUACATECO MIXTECO NAHUATL NA
360 PUREPECHA NAHUATL NA NA
362 MAYA MAZAHUA NA NA
363 OTOMI HUICHOL NA NA
367 NAHUATL MAZAHUA NA NA
368 MIXTECO NAHUATL OTOMI NA
372 PAME HUASTECO NA NA
373 HUICHOL TEPEHUA NA NA
374 MAZAHUA PUREPECHA NA NA
375 ZAPOTECO OTOMI HUICHOL NAHUATL
377 HUASTECO NAHUATL OTOMI NA
378 ZAPOTECO TEPEHUAN HUICHOL NAHUATL
380 MAYA TEPEHUAN ZAPOTECO NA
382 ZAPOTECO TOTONACA NA NA
383 CORA HUICHOL NA NA
387 MIXTECO NAHUATL NA NA
389 HUICHOL CORA NA NA
390 CORA TEPEHUAN NA NA
391 OTOMI MAZAHUA NA NA
404 NAHUATL MAZAHUA MIXTECO NA
406 MAZAHUA NAHUATL NA NA
413 OTOMI MIXE NAHUATL NA
418 HUICHOL MAYA NAHUATL NA
420 MAYA ZAPOTECO NA NA
421 PAME HUASTECO HUICHOL NA
423 HUICHOL MAYA NAHUATL CHONTAL DE OAXACA
424 HUICHOL NAHUATL NA NA
429 NAHUATL PAME NA NA
431 NAHUATL OTOMI HUASTECO ZAPOTECO
432 HUICHOL ZAPOTECO NAHUATL MAYA
434 PUREPECHA NA NA NA
436 NAHUATL MAYA MIXTECO NA
442 HUICHOL NA NA NA
446 PAME CHICHIMECA JONAZ NA NA
451 PUREPECHA MAYA NA NA
455 OTOMI CORA ZAPOTECO NA
456 ZAPOTECO NA NA NA
457 NAHUATL HUASTECO ZAPOTECO NA
458 HUICHOL ZOQUE NA NA
459 HUICHOL MAYA NA NA
461 MAYA PUREPECHA NAHUATL NA
462 CHICHIMECA JONAZ PAME NA NA
463 PAME DEL SUR PAME NA NA
464 NAHUATL HUICHOL NA NA
465 NAHUATL MIXTECO OTOMI NA
469 CORA MAYA NA NA
470 CHICHIMECA JONAZ OTOMI NA NA
477 NAHUATL CHICHIMECA JONAZ MAYA NA
484 NAHUATL TARAHUMARA ZOQUE NA
486 MAYA TZELTAL NA NA
489 PAME ZAPOTECO MAZATECO NA
501 NAHUATL TLAPANECO NA NA
502 MAYA TZELTAL ZAPOTECO NAHUATL
506 HUICHOL HUASTECO MAYA MAZAHUA
510 MAZAHUA YAQUI ZOQUE NAHUATL
515 CHICHIMECA JONAZ NAHUATL TOTONACA NA
516 MAYA ZAPOTECO MAZATECO NA
519 MAYA TOTONACA ZAPOTECO NA
523 HUICHOL CORA MAZATECO NA
531 MAZAHUA CAHITA CHONTAL HUICHOL
535 TOTONACA NA NA NA
546 MAYA MIXE NAHUATL OCUILTECO
547 NAHUATL HUASTECO TOTONACA NA
549 MAYA NAHUATL ZAPOTECO NA
553 PAME COCHIMI NA NA
560 HUICHOL PUREPECHA NA NA
571 MAYA ZAPOTECO OTOMI NA
580 NAHUATL OTOMI HUASTECO TOTONACA
589 NAHUATL MAZATECO HUASTECO MIXTECO
590 MAYA NAHUATL TARAHUMARA NA
591 OTOMI HUASTECO NA NA
593 OTOMI MIXTECO NA NA
598 MAYA CAKCHIQUEL ZAPOTECO NA
599 HUICHOL MIXTECO NA NA
603 OTOMI CHINANTECO NA NA
608 MAYA CHOL ZAPOTECO NA
610 MAYA PUREPECHA MAZAHUA NA
612 OTOMI ZAPOTECO NA NA
620 HUICHOL CORA MAYA NAHUATL
623 NAHUATL HUASTECO OTOMI MAZATECO
630 NAHUATL OTOMI MIXTECO NA
634 MAYA ZOQUE NA NA
641 HUICHOL ZAPOTECO NA NA
643 CORA NA NA NA
649 MAYA PUREPECHA NA NA
656 TZOTZIL HUICHOL OTOMI NA
659 MAYA MIXE NA NA
664 MAYA NAHUATL PUREPECHA NA
671 PUREPECHA ZAPOTECO NA NA
672 HUICHOL NAHUATL MAYA NA
676 OTOMI PUREPECHA NA NA
685 MAYA MAZAHUA NAHUATL PUREPECHA
688 ZOQUE NA NA NA
692 MAZAHUA OTOMI NA NA
696 OTOMI TARAHUMARA NA NA
701 TEPEHUA OTOMI NA NA
703 MAYA MIXE TZELTAL NA
704 MAZATECO ZAPOTECO PUREPECHA PAME
706 OTOMI NAHUATL PUREPECHA NA
711 PUREPECHA HUASTECO NAHUATL ZAPOTECO
717 OTOMI TEPEHUA NA NA
719 TOTONACA OTOMI NA NA
725 OTRAS LENGUAS PUREPECHA NA NA
730 NAHUATL TEPEHUA MIXTECO OTOMI
731 PUREPECHA NAHUATL OTOMI NA
739 HUASTECO HUICHOL MAYA NA
743 MAYA CHONTAL NA NA
746 PUREPECHA HUASTECO NA NA
750 NAHUATL TZELTAL MAYA HUICHOL
754 YAQUI TZOTZIL NA NA
756 HUASTECO MAYA MIXTECO NAHUATL
770 OTOMI NAHUATL TOTONACA NA
772 OTOMI NAHUATL PIMA BAJO NA
778 TOTONACA HUICHOL MAYA MAZAHUA
785 MAZATECO NAHUATL ZAPOTECO NA
788 TZELTAL NA NA NA
792 MAYA POPOLUCA HUASTECO HUICHOL
800 NAHUATL TRIQUI NA NA
801 MAYA CHOL NA NA
802 PUREPECHA MAZAHUA NA NA
806 MAYA TZOTZIL NA NA
823 PUREPECHA OTOMI NA NA
829 MAYA KEKCHI NA NA
836 ZAPOTECO PUREPECHA NA NA
845 OTOMI MAZAHUA PUREPECHA NA
849 OTOMI ZAPOTECO NAHUATL NA
862 PUREPECHA NAHUATL OTOMI TARAHUMARA
863 MAZAHUA NAHUATL PUREPECHA NA
868 MAZAHUA PUREPECHA NAHUATL NA
882 PUREPECHA ZAPOTECO OTOMI MAZAHUA
885 MAYA CHINANTECO DE OJITLAN NA NA
890 HUICHOL PUREPECHA NAHUATL OTOMI
904 OTOMI NAHUATL ZAPOTECO NA
916 OTOMI HUAVE NA NA
921 PUREPECHA MAZAHUA NAHUATL NA
923 NAHUATL OTOMI TOTONACA NA
943 TOTONACA NAHUATL ZAPOTECO NA
952 PUREPECHA MAZAHUA MAYA NA
955 OTOMI ZAPOTECO TOTONACA NA
960 PUREPECHA CORA HUASTECO NA
968 YAQUI ZOQUE NA NA
972 HUASTECO NAHUATL TOTONACA NA
975 PUREPECHA TARAHUMARA NA NA
981 PUREPECHA ZAPOTECO MAYA NA
986 MAYA TOTONACA MAYO NAHUATL
997 MAYA MAME NA NA
998 PUREPECHA CORA NAHUATL OTOMI
1000 PUREPECHA NAHUATL ZAPOTECO TOTONACA
1002 NAHUATL MIXTECO TLAPANECO NA
1003 CHONTAL DE OAXACA TOTONACA NA NA
1012 PUREPECHA CHIAPANECO NA NA
1015 NAHUATL CHINANTECO NA NA
1024 PUREPECHA ZAPOTECO MIXTECO NA
1030 NAHUATL ZAPOTECO TOTONACA NA
1033 MAYA NAHUATL OTOMI NA
1041 NAHUATL TOTONACA TZELTAL ZAPOTECO
1045 ZAPOTECO MAYA OTOMI PUREPECHA
1064 PUREPECHA MAZAHUA ZAPOTECO NA
1071 NAHUATL PUREPECHA HUASTECO NA
1075 MIXTECO OTOMI NA NA
1076 MAYA MAZATECO NA NA
1079 PUREPECHA OTOMI TZOTZIL NA
1090 TOTONACA MAYA NA NA
1091 TOTONACA ZAPOTECO NA NA
1093 OTOMI TZELTAL NA NA
1098 MAYA KANJOBAL NA NA
1102 MAZAHUA MIXE NA NA
1103 CAHITA PUREPECHA TARAHUMARA NA
1105 PUREPECHA IXCATECO OTOMI ZAPOTECO
1110 MAZAHUA TOJOLABAL MAYA NA
1112 NAHUATL OTOMI ZAPOTECO NA
1120 CHINANTECO NA NA NA
1121 HUASTECO TOTONACA NA NA
1123 NAHUATL TZOTZIL NA NA
1129 NAHUATL MIXE ZAPOTECO NA
1132 PUREPECHA MAYA NAHUATL NA
1139 PUREPECHA YUMA NA NA
1157 NAHUATL OTOMI MAZATECO NA
1160 OTOMI MAZAHUA MIXTECO NA
1162 PUREPECHA TARAHUMARA NAHUATL NA
1163 PUREPECHA TLAPANECO NA NA
1165 NAHUATL TOTONACA ZAPOTECO NA
1170 PUREPECHA MIXTECO ZAPOTECO NA
1171 MIXTECO TOTONACA NAHUATL NA
1183 NAHUATL MAZAHUA OTOMI NA
1195 MAZAHUA MAYA NA NA
1202 NAHUATL TOTONACA OTOMI NA
1204 PUREPECHA TOTONACA NA NA
1213 NAHUATL MAYA OTOMI TOTONACA
1231 NAHUATL MIXTECO MAZATECO NA
1253 NAHUATL CHOCHO MAZAHUA MIXTECO
1254 NAHUATL MIXTECO TOTONACA NA
1268 NAHUATL OTOMI MIXTECO MAZATECO
1270 NAHUATL IXCATECO NA NA
1274 PUREPECHA TARAHUMARA HUASTECO NA
1276 PUREPECHA OTOMI ZAPOTECO NA
1278 NAHUATL TOTONACA TZELTAL CHINANTECO
1290 MATLATZINCA NAHUATL NA NA
1299 AMUZGO MIXTECO NA NA
1304 NAHUATL PUREPECHA TOTONACA NA
1315 NAHUATL TOTONACA MAYA NA
1318 NAHUATL MATLATZINCA MIXTECO OTOMI
1322 NAHUATL MAZATECO ZAPOTECO NA
1329 NAHUATL ZAPOTECO OTOMI TARAHUMARA
1340 ZAPOTECO MIXTECO NA NA
1341 NAHUATL ZAPOTECO TOTONACA TARAHUMARA
1354 NAHUATL ZAPOTECO MAYA NA
1357 CHINANTECO MAZATECO NA NA
1358 NAHUATL MAZAHUA TZOTZIL NA
1368 OCUILTECO MAZAHUA NA NA
1374 PUREPECHA KEKCHI MIXE NA
1375 MIXTECO NAHUATL OTOMI TOTONACA
1379 HUASTECO MAYA TOTONACA NA
1380 ZAPOTECO ZOQUE NA NA
1387 NAHUATL MAYA PUREPECHA NA
1388 OTOMI MAZAHUA NAHUATL NA
1389 CHOL MAYA NA NA
1390 NAHUATL ZAPOTECO TOTONACA MAYA
1393 NAHUATL OTOMI MAZATECO ZAPOTECO
1396 NAHUATL MAZATECO NA NA
1397 NAHUATL OTOMI MIXE NA
1398 NAHUATL HUASTECO MAYA NA
1403 NAHUATL OTRAS LENGUAS NA NA
1407 NAHUATL CORA PUREPECHA NA
1410 NAHUATL ZAPOTECO MAZATECO NA
1414 NAHUATL CHOCHO NA NA
1421 CHINANTECO CHINANTECO DE OJITLAN NA NA
1425 NAHUATL PUREPECHA MAYA NA
1426 NAHUATL CHOCHO CHINANTECO NA
1427 NAHUATL ZAPOTECO MIXTECO MAZATECO
1434 NAHUATL MIXE MIXTECO TOTONACA
1435 OTOMI MAYA MIXTECO NA
1440 NAHUATL ZAPOTECO MIXTECO NA
1442 NAHUATL TLAPANECO TEPEHUA MAZAHUA
1443 ZAPOTECO MIXTECO NAHUATL NA
1447 NAHUATL PUREPECHA OTOMI MIXTECO
1448 NAHUATL CHONTAL DE OAXACA MAZATECO NA
1457 NAHUATL TOTONACA MIXTECO NA
1462 NAHUATL ZAPOTECO OTOMI MAYA
1470 NAHUATL TLAPANECO TOTONACA MIXE
1473 NAHUATL CHINANTECO MAZAHUA ZAPOTECO
1477 MAYA MIXTECO DE LA MIXTECA BAJA NAHUATL NA
1484 CHINANTECO CHINANTECO DE OJITLAN MAYA NA
1489 CAKCHIQUEL NAHUATL NA NA
1490 ZAPOTECO POPOLUCA NAHUATL NA
1493 NAHUATL CHINANTECO MAZATECO NA
1494 CHOCHO NAHUATL NA NA
1503 TZOTZIL NA NA NA
1508 NAHUATL MIXE NA NA
1513 NAHUATL MIXTECO TZOTZIL NA
1530 CHOCHO POPOLUCA NA NA
1531 NAHUATL MIXE TOTONACA NA
1538 NAHUATL TARAHUMARA MAZAHUA NA
1541 CHONTAL CHONTAL DE TABASCO NA NA
1542 MIXTECO TOTONACA NA NA
1543 MIXTECO NA NA NA
1546 CHINANTECO DE OJITLAN ZAPOTECO NA NA
1547 MIXTECO MAYA NA NA
1552 NAHUATL PUREPECHA MIXTECO NA
1555 NAHUATL CHATINO TOTONACA NA
1557 MAZATECO NAHUATL NA NA
1563 CHINANTECO ZAPOTECO NA NA
1567 TLAPANECO NAHUATL NA NA
1570 POPOLUCA NAHUATL NA NA
1573 MAZATECO NAHUATL MIXTECO NA
1578 OTOMI TLAPANECO NAHUATL MIXE
1579 MIXTECO ZAPOTECO MAYA NA
1584 CHONTAL DE TABASCO CHONTAL NA NA
1586 NAHUATL TOTONACA MAZAHUA NA
1587 NAHUATL MIXE MIXTECO ZAPOTECO
1590 NAHUATL TLAPANECO OTOMI NA
1593 NAHUATL CHONTAL DE TABASCO NA NA
1594 MIXTECO CHOCHO NA NA
1602 MIXTECO CHOCHO TOTONACA NA
1603 MAZATECO CHINANTECO NA NA
1605 MIXTECO NAHUATL MIXTECO DE LA MIXTECA BAJA MAYA
1608 CHINANTECO MIXTECO NA NA
1614 MIXTECO TOTONACA ZAPOTECO NA
1618 POPOLUCA POPOLUCA DE TEXISTEPEC NA NA
1622 MAZATECO MAME NA NA
1624 MIXTECO ZAPOTECO MAZATECO MIXE
1626 MIXTECO MIXTECO DE LA MIXTECA BAJA NA NA
1633 ZAPOTECO CHINANTECO NA NA
1634 MIXTECO AMUZGO NA NA
1635 NAHUATL AMUZGO TARAHUMARA NA
1636 MAZATECO NA NA NA
1638 MAZATECO MIXTECO NA NA
1640 MAZATECO ZAPOTECO NAHUATL MIXTECO
1646 MAZATECO ZAPOTECO NA NA
1648 CHONTAL MAYA NA NA
1650 MIXTECO MAYA NAHUATL NA
1658 MAZATECO MIXE NA NA
1662 MAZATECO MIXTECO NAHUATL ZAPOTECO
1669 MIXTECO HUASTECO MAZATECO NAHUATL
1671 POPOLUCA ZAPOTECO NA NA
1682 MIXTECO MAZATECO NA NA
1685 TRIQUI NA NA NA
1702 MAZATECO CUICATECO NA NA
1705 MIXTECO CUICATECO NA NA
1706 CUICATECO CHINANTECO NA NA
1710 MIXTECO TRIQUI NA NA
1716 ZOQUE TZOTZIL NA NA
1719 CHINANTECO NAHUATL NA NA
1720 POPOLUCA CHOCHO NA NA
1721 CHINANTECO CUICATECO NA NA
1728 CUICATECO MIXTECO NA NA
1738 CHOL TZELTAL NA NA
1741 IXCATECO MIXTECO NA NA
1746 ZAPOTECO MAZATECO NA NA
1751 CHOCHO MIXTECO NA NA
1754 MIXTECO MAYA ZAPOTECO SUREÑO NA
1756 NAHUATL ZAPOTECO MIXTECO KEKCHI
1758 MIXTECO NAHUATL MAZATECO NA
1760 CUICATECO NA NA NA
1766 ZOQUE CHOL NA NA
1781 MIXTECO CHOCHO MAZATECO NA
1786 CHOCHO NA NA NA
1800 ZAPOTECO MIXE NA NA
1803 MIXTECO CHOCHO NAHUATL ZAPOTECO
1808 MIXTECO SOLTECO CHINANTECO NA
1817 ZOQUE MIXTECO NA NA
1818 CHOL TZOTZIL NA NA
1823 MIXTECO ZAPOTECO NAHUATL NA
1825 TZELTAL CHOL NA NA
1831 MIXTECO CHOCHO ZAPOTECO NA
1839 MIXTECO TLAPANECO NA NA
1845 MIXE MAZATECO NA NA
1850 CHINANTECO MIXE NA NA
1853 MIXTECO MIXE NA NA
1865 MIXTECO CHATINO NA NA
1867 TZOTZIL ZOQUE NA NA
1870 MIXTECO MIXE ZAPOTECO NA
1890 MIXTECO TRIQUI ZAPOTECO NA
1896 TLAPANECO ZAPOTECO NA NA
1899 ZAPOTECO ZAPOTECO DE IXTLAN NA NA
1916 ZAPOTECO CHINANTECO NAHUATL NA
1932 ZOQUE TZELTAL NA NA
1937 MIXE MIXTECO NA NA
1943 TZOTZIL CHOL NA NA
1944 TLAPANECO MIXTECO NA NA
1948 MIXTECO MIXE NAHUATL NA
1949 MIXTECO CHONTAL TRIQUI ZAPOTECO
1962 ZAPOTECO CHINANTECO MIXTECO NA
1966 TZOTZIL TZELTAL NA NA
1975 ZOQUE TZELTAL TOTONACA NA
1983 ZAPOTECO CHINANTECO MIXE NA
1984 TRIQUI MIXTECO NA NA
2009 MIXE NA NA NA
2013 ZAPOTECO MIXE MIXTECO MAYA
2023 MIXTECO TRIQUI MAZAHUA ZAPOTECO
2025 ZAPOTECO MIXE MIXTECO NAHUATL
2031 MIXE ZAPOTECO MIXTECO NA
2045 MIXE TOTONACA ZAPOTECO NA
2046 TZOTZIL ZAPOTECO NA NA
2047 TZELTAL TZOTZIL NA NA
2049 MIXE CHINANTECO NA NA
2055 MIXE MIXTECO ZAPOTECO NA
2059 MIXE NAHUATL NA NA
2065 MIXTECO TRIQUI MIXE NA
2073 MIXTECO CHINANTECO NA NA
2084 ZAPOTECO VALLISTA ZAPOTECO NA NA
2092 TZOTZIL ZOQUE TZELTAL NA
2101 MIXTECO NAHUATL MAZATECO ZAPOTECO
2117 MIXTECO MIXE NAHUATL TRIQUI
2125 ZAPOTECO CHATINO NA NA
2144 ZAPOTECO CUICATECO NA NA
2146 ZAPOTECO ZAPOTECO VALLISTA NA NA
2151 TZELTAL CHOL TZOTZIL NA
2152 CHATINO NA NA NA
2154 ZAPOTECO CHONTAL DE OAXACA NA NA
2156 MIXTECO CHINANTECO CHOCHO NA
2159 MIXTECO DE LA MIXTECA BAJA MIXTECO NA NA
2162 ZAPOTECO HUAVE NA NA
2163 ZAPOTECO MAZATECO NAHUATL NA
2167 TZELTAL TOJOLABAL NA NA
2175 ZAPOTECO TZOTZIL NA NA
2198 MIXTECO ZAPOTECO AMUZGO NA
2201 ZAPOTECO OCUILTECO NA NA
2210 ZAPOTECO ZAPOTECO SUREÑO NA NA
2218 ZAPOTECO CHONTAL MIXE NA
2220 ZAPOTECO MAYA TZELTAL TZOTZIL
2221 TZOTZIL POPOLUCA NA NA
2222 TOJOLABAL KANJOBAL NA NA
2234 ZAPOTECO TZELTAL TRIQUI MIXE
2242 CHATINO CHINANTECO MIXTECO NA
2243 ZAPOTECO SUREÑO ZAPOTECO NA NA
2244 TZOTZIL TOJOLABAL NA NA
2249 MIXE CHONTAL NA NA
2252 TZELTAL TOJOLABAL TZOTZIL NA
2258 MIXTECO MIXTECO DE LA COSTA NA NA
2274 CHATINO ZAPOTECO NA NA
2291 HUAVE ZAPOTECO NA NA
2297 CHONTAL CHONTAL DE OAXACA NA NA
2308 ZAPOTECO NAHUATL OTOMI ZAPOTECO SUREÑO
2311 CHONTAL DE OAXACA CHONTAL NA NA
2312 CHATINO MIXTECO NA NA
2315 MIXTECO TLAPANECO AMUZGO NA
2316 ZAPOTECO CHONTAL NA NA
2349 ZAPOTECO MIXTECO TRIQUI NA
2350 KANJOBAL TOJOLABAL NA NA
2354 CHATINO CHINANTECO NA NA
2367 ZAPOTECO HUASTECO ZAPOTECO DEL ISTMO NA
2369 CHONTAL ZAPOTECO NA NA
2375 KANJOBAL TZOTZIL NA NA
2376 MAME JACALTECO NA NA
2381 ZAPOTECO TZELTAL NA NA
2383 MAME KANJOBAL NA NA
2386 MAME ZAPOTECO NA NA
2387 MAME TZOTZIL CAKCHIQUEL NA
2388 MAME TZELTAL NA NA
2390 MAME CAKCHIQUEL NA NA
2391 MAME MOTOCINTLECO NA NA
2395 ZAPOTECO MAME NA NA
2396 MAME TZOTZIL NA NA
2399 MAME ZAPOTECO TZELTAL NA
2404 MAME ZAPOTECO CHONTAL DE OAXACA NA
2405 ZAPOTECO QUICHE NA NA

Purpose Statements

  1. Model current relationships between maize landraces and their niches
    • Project maize niches into geographic space
    • Forecast these projections with future climate models
  2. Identify patterns
    • Evaluate spatio-temporal relationships between indigeneity and maize diversity
    • Quantify maize landrace diversity and it's projected change
    • Identify similarities among species occurrence patterns
  3. Provide the groundwork for future work

Research Questions

  1. How do social and environmental factors contribute to maize landraces' current distribution?
  2. What are the characteristics of each maize landraces ''bioclimate envelope''?
  3. Can the contributions of indigeneity and society to maize landraces distribution and diversity be discerned and modelled at the national level?
  4. What is the nature of the relationship between maize landrace diversity and indigeneity in Mexico?
  5. How is climate change projected to alter current maize landrace distributions?
  6. Which maize landraces are suseptible of extinction/expansion?

Ecological Niche


Source: (Edmondson, 1971)


  • Joseph Grinnell (Grinnell, 1917) - niches corresponded to species' environments
  • Charles Elton (Elton, 1927) - associated species niches through biotic relationships
  • George Evelyn Hutchinson - species role in environment (Hutchinson, 1957; Colwell and Rangel 2009).
    • The Hutchinsonian Niche - an “n-dimensional hypervolume” in ecological space in which a species can persist (Colwell and Rangel 2009; Holt 2009)
    • Fundmental Niche - A species' potential geographic distribution in true 4D space, constrained by its n-dimensional bioclimatic envelope
    • Realized Niche - fundamental niche constrained by 'bionomic' interactions; the niche that exists in reality and that does not overlap with other species niches (Griesemer 1992,Pulliam 2000)

Biotic-Abiotic-Mobility Diagram


Source: (Guisan et al., 2017) Credit: (Guisan et al., 2017,A. Townsend Peterson et al. 2011; Soberon & Peterson, 2005)



  • G : Studied geographic area
    • A : Suitable abiotic environment (Grinellean niche)
    • B : Suitable biotic environment (Eltonian niche)
    • C : Colonizable range
  1. Realize Niche, suitable to all three (Hutchinson's Niche)
  2. Sutiable abiotic with unsuitable biotic conditions (e.g.: high competition)
  3. Colonization outside of suitable environment (sink populations)
  4. Sink in unsuitable biotic and abiotic environments (historical effects)

Ecological Niche Modelling



  • Uses species presence and/or absence observation data and ecological data at those observation sites to estimate complex relationships of species niche (Elith et al. 2011).
  • Project hyperdimensional ecological space onto 2- or 3D geographic space
  • Numerous algorithms developed for and adapted to ENM
  • Ensemble Ecological Niche Modelling weights individual models based on performance(Filho et. al, 2010, Araujo & New 2007)
  • Reduce uncertainty across model type, model repetition, Pseudo-absence selection, etc.
  • WA and Mean consensus methods provided significantly more robust predictions than single-models and the other consensus methods (Marimon et al., 2009)

Data

  1. Georeferenced maize observations
  2. Ethno-linguistic Data
  3. Gridded Climatologies
    • derived “Bioclimatic Variables”
  4. Topography/Land Cover

Georeferenced Maize Observations



  • 22931 available through La Comision Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO)
  • Cleaned records of erroneous records and those flagged 'Inconsistent'
  • 18060 remaining records
  • 64 unique maize races

plot of chunk maizehisto&countplot of chunk maizehisto&count

Maize Observations

plot of chunk unnamed-chunk-4

Climatological Data - WorldClim


  • ANUSPLIN up to 60,000 weather stations (with >10 years data)
  • largest available archive of downscaled climate data for ecology (Guisan et al., 2017)
  • >2,700 citations for ecological studies including ENMs and SDMs (Booth et al., 2014)
  • 30 arc-second resolution (~1 km2)
  • Current (1970-2000) (v2.0) (Fick & Hijmans, 2017)
    • Maximum and Minimum Monthly Temperatures (°C * 10)
    • Monthly Precipitation (mm)
    • also includes solar radiation, windspeed, water vapor pressure
  • Future RCP 8.5 (2041-2060, 2061-2080) (v1.4) (Hijmans et al., 2005)
    • dynamically-downscaled from 19 GCM used in CMIP5 (IPCC, 2013, Meehl et al., 2009)

plot of chunk 2050precStackplot of chunk 2050precStack

Projecting onto Future Climatologies

  • Ensemble future climate models to reduce uncertainty across:
    • Relative Concentration Pathways
    • GCM
  • Monthly future climatologies were averaged across five GCMs:
    • CCSM4 (Community Climate System Model, UCAR)
    • MIROC5 (Model for Interdisciplinary Research on Climate)
    • MPI-ESM-LR (Max-Plank Institute)
    • HADGEM2-ES (Met Office Hadley)
    • GFDL-CM3 (Geophysical Fluid Dynamics Laboratory )
  • Any of these five model performs better singularly than ensemble of all other models in predicting previous climatologies (Conde et al. 2011)
  • Here, using updated analogs of GCMs used in (Conde et al. 2011) & 5th National Communication of Mexico for the United Nations Framework Convention on Climate Change (2012)

Bioclimatic Variables


  • Derived from first ecological niche modelling alogorithm 'BIOCLIM' (Nix, 1986)
  • More ecologically importance predictor variables
  • Can reduce multicollineary, VIF; expands options
  • Calculated from monthly Tmax, Tmin, Tmean, and Prec
  • 19 Bioclimatic proxy variables from monthly means using 'dismo' (Hijmans et al., 2017)
  • Potentially 16 'complementary' variables from 'envirem' package in R (Title & Bemmels, 2017)
    • Must use (WorldClim 2.0) current terrestial solar radiation for both current and future climatologies
    • Must average Tmin and Tmax to get Tmean (.99 R2) (Title & Bemmels, 2017)

1970-2000 Bioclimatic Variables plot of chunk unnamed-chunk-5plot of chunk unnamed-chunk-5

'dismo' Biovars


  • BIO1 = Annual Mean Temperature
  • BIO2 = Mean Diurnal Range (Mean of monthly (max temp - min temp))
  • BIO3 = Isothermality (BIO2/BIO7) (* 100)
  • BIO4 = Temperature Seasonality (standard deviation *100)
  • BIO5 = Max Temperature of Warmest Month
  • BIO6 = Min Temperature of Coldest Month
  • BIO7 = Temperature Annual Range (BIO5-BIO6)
  • BIO8 = Mean Temperature of Wettest Quarter
  • BIO9 = Mean Temperature of Driest Quarter
  • BIO10 = Mean Temperature of Warmest Quarter


  • BIO11 = Mean Temperature of Coldest Quarter
  • BIO12 = Annual Precipitation
  • BIO13 = Precipitation of Wettest Month
  • BIO14 = Precipitation of Driest Month
  • BIO15 = Precipitation Seasonality (Coefficient of Variation)
  • BIO16 = Precipitation of Wettest Quarter
  • BIO17 = Precipitation of Driest Quarter
  • BIO18 = Precipitation of Warmest Quarter
  • BIO19 = Precipitation of Coldest Quarter

'envirem' Biovars


  • Annual PET
  • Thornthwaite Aridity Index
  • Climatic Moisture Index
  • Continentality
  • EmbergerQ
  • Growing Deg Days 0
  • Growing Deg Days 5
  • Growing Deg Days 10
  • Max Temp Coldest
  • Min Temp Warmest


  • Month Count By Temp 10
  • PET Coldest Quarter
  • PET Driest Quarter
  • PET seasonality
  • PET Warmest Quarter
  • PET Wettest Quarter
  • Thermicity Index

Correlation Matrix - 'dismo' and 'envirem'

Topographic Variable Processing


  • Terrain Indices from Elevation from WorldClim 1.4 via SRTM (Shuttle Radar Topographic Mission) with 'raster' package (Hijmans, 2017) (Wilson et al., 2007)
    • Aspect
    • Slope
    • Roughness (difference between the maximum and the minimum value of a cell and its 8 surrounding cells)



plot of chunk topostack

FAO Harmonized Soil Database 1.2


30 arc-second resolution (Fischer et al., 2008)

  • Soil Quality Data (Factor, 'No or slight limitations' to 'Very severe limitations')
    1. Nutrient availability
    2. Nutrient retention capacity
    3. Rooting conditions
    4. Oxygen availability to roots
    5. Excess salts
    6. Toxicity
    7. Workability (constraining field management)



  • Land cover (Continuous)
    1. rain-fed cultivated land
    2. irrigated cultivated land, according to GMIA 4.0
    3. total cultivated land
    4. forest land, calibrated to FRA2000 land statistics
    5. grass/scrub/woodland
    6. built-up land (residential and infrastructure)
    7. barren/very sparsely vegetated land
    8. Mapped Water Bodies

Soil Quality

plot of chunk soilstack

Landcover

plot of chunk lcstack

Variables to Thin


  • Land Cover Classification
  • Soil Quality 1-7
  • 19 'dismo' bioclimatic variables
  • 16 supplemental 'envirem' bioclimatic variables

Method

  • 'usdm' R package for vif objects (Naimi et al., 2014)
  • preferred over correlation thresholds due to 'hidden' correlation structures (Guisan et al., 2017)
  • correlation structures may change over time
  • select variables of ecological importance
  • 'vifstep' with VIF threshold of 10 to get remaining variables (Guisan et al., 2017)
# using all data, no pre-selection
presvifstep@results
               Variables      VIF
1      BIO8MeanTWettestQ 3.169749
2  BIO13PrecWettestMonth 7.203552
3      BIO15PrecSeas.COV 5.426713
4      BIO18PrecWarmestQ 5.112587
5      BIO19PrecColdestQ 6.479971
6      EVMminTempWarmest 3.258270
7     EVMmonthCountByT10 2.206976
8         EVMPETColdestQ 5.539127
9          EVMPETDriestQ 2.507005
10        EVMPETWarmestQ 4.670706
11               IrrCult 1.169090
12          Rain.fedCult 1.599255
13        Grass.Woodland 2.314950
14                Barren 1.730909
15                 Urban 1.075483
16                 Water 1.107406
17                aspect 1.023113
18                 slope 1.446115

'vifcor' results

presvifcor@results
              Variables       VIF
1  BIO2MeanDiurnalRange 18.522678
2          BIO3Isotherm 23.926854
3     BIO8MeanTWettestQ  3.749390
4      BIO9MeanTDriestQ 23.234599
5     BIO15PrecSeas.COV  6.459824
6     BIO18PrecWarmestQ  3.463208
7     BIO19PrecColdestQ  7.060739
8             EVMannPET 55.203847
9     EVMminTempWarmest 28.195976
10   EVMmonthCountByT10  2.540841
11        EVMPETDriestQ 15.802205
12       EVMPETWarmestQ 45.516679
13   EVMthermicityIndex 13.293752
14              IrrCult  4.539498
15         Rain.fedCult 14.357032
16             Forested 45.296046
17       Grass.Woodland 40.844532
18               Barren 15.374231
19                Urban  1.777139
20                Water  2.225376
21               aspect  1.027214
22                slope  1.546577

Proposed Methods

  • Ecological Niche modelling with biomod2 with up to
    • 11 modelling algorithms
      • 'GLM','GBM','GAM','CTA','ANN','SRE','FDA','MARS',
        'RF','MAXENT.Phillips', 'MAXENT.Tsuruoka'
    • 10 evaluation metrics
      • 'KAPPA', 'TSS', 'ROC', 'FAR', 'SR', 'ACCURACY', ,
        'BIAS', 'POD', 'CSI' and 'ETS'
    • n modelling repetitions (~ 5 proposed)
    • n PA pseudo-absence selection and repetition (undecided)
  • Modelling with 50% data split for evaluation
  • Ensemble landrace models by weighted-average and committee average by model
  • Average weighted-averages across models
  • Parallel process on TxState LEAP HPC Cluster with OpenMpi, Rmpi, 'snow', SLURM
    • 64 nodes with 28 CPUs (3584 workers + 1 master CPU) each up to 128 Gb RAM!

Pseudo-absence selection Recommendations

Barbet-Massin et al. 2012

  1. ~ 10,000 of pseudo-absences when using regression techniques
  2. ~ 10 runs with ~100 pseudo-absences with multiple adaptive regression splines and discriminant analyses;
  3. Equal number of pseudo-absences/presences with ~10 runs for classification techniques (e.g.: BRT, CART and RF).
  4. Random selection of pseudo-absences when using regression techniques
  5. Environmentally stratified pseudo-absences when using classification and machine-learning techniques

Potential Subsequent Analyses

  • Binary distribution maps using probability thereshold
  • Alpha-Diversity (sum binary predictions)
  • Range Change/Extinction Rate (biomod2)
  • Diversity Indices (and change) (Sorenson)
  • Evaluation across model type, maize landrace (boxplots)
  • Evaluation of Importance of Indigeneity (Variable importance in 'biomod2')
  • Cluster Analysis of Variable Importance
  • Dendrogram of distribution similarities ('fuzzySim')
  • Geovisulation of Impacted Indigenous Communities

Novelty of Research

Methods

  • Ensemble modelling
  • Expanded datasets
  • Averaging good-performing updated GCMs for forcasting
  • CMIP5 Data
  • Careful pseudo-absence selection
  • WorldClim 2.0 > 1.4

Contributions

  • Geographic representation of climate change impacts on maize biodiversity at COD
  • Visualize locations of impacted communities

Bibliography

Anderson, E. 1947. Field Studies of Guatemalan Maize. Annals of the Missouri Botanical Garden 34 (4):433–467. http://www.jstor.org/stable/2394775.

Anderson, E., and H. C. Cutler. 1942. Races of Zea Mays: I. Their Recognition and Classification. Annals of the Missouri Botanical Garden 29 (2):69–88. http://www.jstor.org/stable/2394331.

Beatriz Rendón-Aguilar, Verónica Aguilar-Rojas, María del Consuelo Aragón- Martínez, José Francisco Ávila-Castañeda, Luis Alberto Bernal-Ramírez, David Bravo-Avilez, Guadalupe Carrillo-Galván, Amelia Cornejo-Romero, Ernesto Delgadillo-Durán, Gilberto Hern, R. O.-P. 2015. DIVERSIDAD DE MAÍZ EN LA SIERRA SUR DE OAXACA, MÉXICO: CONOCIMIENTO Y MANEJO TRADICIONAL.

Brown, M. E., and C. C. Funk. 2008. Climate. Food security under climate change. Science (New York, N.Y.) 319:580–581.

Buckler IV, E. S., M. M. Goodman, T. P. Holtsford, J. F. Doebley, and J. Sanchez G. 2006. Phylogeography of the wild subspecies of Zea mays. Maydica 51 (1):123–134.

Cheng, J., M. Mattiuzzi, M. Sumner, J. A. Greenberg, A. Bevan, A. Shortridge, and M. R. J. Hijmans. 2016. Package “ raster .”

Colwell, R. K., and T. F. Rangel. 2009. Hutchinson’s duality: The once and future niche. Proceedings of the National Academy of Sciences.

CONDE, C., C. CONDE, F. ESTRADA, B. MARTÍNEZ, O. SÁNCHEZ, and C. GAY. 2011. Regional climate change scenarios for México. Atmósfera 24 (1):125–140. http://www.revistascca.unam.mx/atm/index.php/atm/article/view/23806.

Conde, C., R. Ferrer, and S. Orozco. 2006. Climate change and climate variability impacts on rainfed agricultural activities and possible adaptation measures. A Mexican case study. Atmosfera 19 (3):181–194.

Cutler, E. A. and H. C. . 1942. Races of Zea Mays : I . Their Recognition and Classification. Annals of the Missouri Botanical Garden 29 (2):69–86+88. http://www.jstor.org/stable/2394331.

Diniz Filho, J. A. F., V. G. V. G. Ferro, T. Santos, J. C. Nabout, R. Dobrovolski, P. De Marco Jr., J. D. Filho, V. G. V. G. Ferro, J. A. F. Diniz, T. Santos, J. C. Nabout, R. Dobrovolski, and P. de Marco. 2010. The three phases of the ensemble forecasting of niche models: geographic range and shifts in climatically suitable areas of Utetheisa ornatrix (Lepidoptera,. Revista Brasileira de Entomologia 54 (3):339–349. d:%5Cbiblio%5Cd%5C24483.pdf%5Cnhttp://www.scielo.br/scielo.php?pid=S0085-56262010000300001&script=sci_arttext.

Dyer, G. A., and A. López-Feldman. 2013. Inexplicable or Simply Unexplained? The Management of Maize Seed in Mexico. PLoS ONE.

E.J. Wellhausen, L.M. Roberts, E.Hernandez X., Paul C, M. 1952. Races of Maize in Mexico. The Bussey Institution of Harvard University.

Elith, J., C. H. Graham, R. P. Anderson, M. Dudik, S. Ferrier, A. Guisan, R. J. Hijmans, F. Huettmann, J. R. Leathwick, A. Lehmann, J. Li, L. G. Lohmann, B. A. Loiselle, G. Manion, C. Moritz, M. Nakamura, Y. Nakazawa, J. M. Overton, A. T. Peterson, S. J. Phillips, K. Richardson, R. Scachetti-Pereira, R. E. Schapire, J. Soberon, S. Williams, M. S. Wisz, and N. E. Zimmermann. 2006. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29 (2):129–151.

Elith, J., M. Kearney, and S. Phillips. 2010. The art of modelling range-shifting species. Methods in Ecology and Evolution 1 (4):330–342. http://dx.doi.org/10.1111/j.2041-210X.2010.00036.x%5Cnhttp://onlinelibrary.wiley.com/store/10.1111/j.2041-210X.2010.00036.x/asset/j.2041-210X.2010.00036.x.pdf?v=1&t=hrlhwru3&s=48e17a701a52d91fbecae111ba2dfea8a8912197.

Elith, J., J. R. Leathwick, and T. Hastie. 2008. A working guide to boosted regression trees. Journal of Animal Ecology 77 (4):802–813.

Elith, J., T. Phillips, StevenHastie, M. Dudík, Y. E. Chee, and C. J. Yates. 2011. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions 17 (1):43–57.

Elton, C. S. 1927. Animal Ecology. Animal ecology :1–260. http://books.google.com/books?hl=fr&lr=&id=lZvgTuB9Gh4C&pgis=1%5Cnhttp://www.cabdirect.org/abstracts/19632204195.html%5Cnhttp://www.mendeley.com/research/animal-ecology-14/%5Cnhttp://www.cabdirect.org/abstracts/19632204195.html.

Esquinas-Alcázar, J. 2005. Science and society: protecting crop genetic diversity for food security: political, ethical and technical challenges. Nature reviews. Genetics 6 (12):946–53. http://www.ncbi.nlm.nih.gov/pubmed/16341075%5Cnhttp://dx.doi.org/10.1038/nrg1729%5Cnhttp://www.nature.com/nrg/journal/v6/n12/abs/nrg1729.html.

Fick, S. E., and R. J. Hijmans. 2017. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology.

Garcia-Marmolejo, G., L. Chapa-Vargas, E. Huber-Sannwald, M. Weber, O. C. Rosas-Rosas, J. Martinez-Carderas, H. F. López-Arévalo, S. Gallina, R. Landgrave, E. Martínez-Meyer, L. E. Muñoz-Villers, S. a Queenborough, M. R. Metz, T. Wiegand, R. Valencia, W. Oliver, A. Fuller, T. Dawson, B. Helmuth, R. S. Hetem, D. Mitchell, S. K. Maloney, P. Illoldi-Rangel, V. Sánchez-Cordero, A. Townsend Peterson, S. Albert, C. A. Ramotnik, C. G. Schmitt, S. Albert, C. A. Ramotnik, A. L. J. Desbiez, S. A. Santos, A. Keuroghlian, R. E. Bodmer, L. Siege, J. Elith, S. J. Phillips, T. Hastie, M. Dudík, Y. E. Chee, C. J. Yates, J. Meerman, D. Norris, F. Rocha-Mendes, S. Frosini de Barros Ferraz, J. P. Villani, and M. Galetti. 2007. Towards New Scenarios for Analysis of Emissions, Climate Change, Impacts, and Response Strategies. Geneva.

García-Martínez, Y. G., C. Ballesteros, H. Bernal, O. Villarreal, L. Jiménez-García, and D. Jiménez-García. 2016. Traditional agroecosystems and global change implications in Mexico. Bulgarian Journal of Agricultural Science.

Guisan, A., and W. Thuiller. 2005. Predicting species distribution: Offering more than simple habitat models. Ecology Letters 8 (9):993–1009.

Hijmans, A. R. J., S. Phillips, J. Leathwick, J. Elith, and M. R. J. Hijmans. 2017. Package “ dismo .”

Hijmans, R. J., S. E. Cameron, J. L. Parra, G. Jones, and A. Jarvis. 2005. VERY HIGH RESOLUTION INTERPOLATED CLIMATE SURFACES FOR GLOBAL LAND AREAS. 1978:1965–1978.

Hoegh-Guldberg, O., and J. F. B. 2010. The Impact of Climate Change on the World’s Marine Ecosystems. Science 328 (5985):1523–1528.

Holt, R. D. 2009. Bringing the Hutchinsonian niche into the 21st century: Ecological and evolutionary perspectives. Proceedings of the National Academy of Sciences 106 (Supplement 2):19659–19665. http://www.pnas.org/content/106/suppl.2/19659.abstract%5Cnhttp://www.pnas.org/content/106/suppl.2/19659.full.pdf.

IPCC. 2007. Mitigation of climate change: Contribution of working group III to the fourth assessment report of the Intergovernmental Panel on Climate Change.

———. 2013. Climate Change 2013.

Kang, Y, S. Khan, X. M. 2009. Climate Change Impacts on Crop Yied, Crop Water, Productivity and Food Secury - A Review. Progress in Natural Science 19 (12):1665–1674.

Llovizna González Martínez, S., L. Arturo, Á. Meléndez, J. Teodoro, S. García, and G. B. Wells. 2015. Comunidades indígenas: Entre la adaptación a alteraciones climáticas locales y el abandono de la agricultura. Enero -Abril :27–48.

Mastrandrea, M. D., K. J. Mach, V. R. Barros, T. E. Bilir, D. J. Dokken, O. Edenhofer, C. B. Field, T. Hiraishi, S. Kadner, T. Krug, J. C. Minx, R. Pichs-madruga, G. Plattner, D. Qin, Y. Sokona, T. F. Stocker, and M. Tignor. 2015. IPCC Expert Meeting on Climate Change , Food , and Agriculture Edited by : IPCC Expert Meeting on Climate Change , Food , and Agriculture.

Miguel B Araújo, A. T. P. 2008. Uses and misuses of bioclimatic envelope modeling. Ecology 89 (10):2712–2724.

Monterroso Rivas, A. I., C. Conde Álvarez, G. Rosales Dorantes, J. D. Gómez Díaz, and C. Gay García. 2011. Assessing current and potential rainfed maize suitability under climate change scenarios in M??xico. Atmosfera 24 (1):53–67.

Nuss, E. T., and S. A. Tanumihardjo. 2010. Maize: A paramount staple crop in the context of global nutrition. Comprehensive Reviews in Food Science and Food Safety 9 (4):417–436.

Peterson, A. T., and J. Soberón. 2012. Species distribution modeling and ecological niche modeling: Getting the Concepts Right. Natureza a Conservacao.

Ramirez-Cabral, N. Y. Z., L. Kumar, and F. Shabani. 2017. Global alterations in areas of suitability for maize production from climate change and using a mechanistic species distribution model (CLIMEX). Scientific Reports 7 (1):5910. http://www.nature.com/articles/s41598-017-05804-0.

Rivero-Romero, A. D., A. I. Moreno-Calles, A. Casas, A. Castillo, and A. Camou-Guerrero. 2016. Traditional climate knowledge: a case study in a peasant community of Tlaxcala, Mexico. Journal of Ethnobiology and Ethnomedicine 12.

Ruiz Corral, J. A., N. Durán Puga, J. D. J. Sánchez González, J. Ron Parra, D. R. González Eguiarte, J. B. Holland, and G. Medina García. 2008. Climatic adaptation and ecological descriptors of 42 Mexican maize races. Crop Science 48 (4):1502–1512. Society, A. O., and T. Auk. 2017. The Niche-Relationships of the California Thrasher Author ( s ): Joseph Grinnell Source : The Auk , Vol . 34 , No . 4 ( Oct ., 1917 ), pp . 427-433 Published by : American Ornithological Society Stable URL : http://www.jstor.org/stable/4072271. 34 (4):427–433.

Thrupp, L. A. 2000. Linking Agricultural Biodiversity and Food Security: The Valuable Role of Sustainable Agriculture. International Affairs (Royal Institute of International Affairs 1944-) 76 (2):265–281. http://www.jstor.org/stable/2626366.

Thuiller, W., B. Lafourcade, R. Engler, and M. B. Araújo. 2009. BIOMOD - A platform for ensemble forecasting of species distributions. Ecography 32 (3):369–373.

Title, P. O., and J. B. Bemmels. 2017. ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. Ecography (January):1–16. Toledo, V., and N. Barrera-Bassols. 2017. Political Agroecology in Mexico: A Path toward Sustainability. Sustainability.

Ureta, C., C. González-Salazar, E. J. González, E. R. Álvarez-Buylla, and E. Martínez-Meyer. 2013. Environmental and social factors account for Mexican maize richness and distribution: A data mining approach. Agriculture, Ecosystems and Environment.

Ureta, C., E. Martínez-Meyer, E. J. González, and E. R. Álvarez-Buylla. 2015. Finding potential high-yield areas for Mexican maize under current and climate change conditions. Journal of Agricultural Science :1–13.

Ureta, C., E. Martínez-Meyer, H. R. Perales, and E. R. Álvarez-Buylla. 2012. Projecting the effects of climate change on the distribution of maize races and their wild relatives in Mexico. Global Change Biology.

Walther, G., E. Post, P. Convey, A. Menzel, C. Parmesan, T. J. C. Beebee, J. Fromentin, O. H. I, and F. Bairlein. 2002. Ecological responses to recent climate change. Nature 416.

Zimmermann, Niklaus E. Thuiller, Wilfried Guisan, A. 2017. Habitat Suitability and Distribution Models with Applications in R 1st ed. ed. J. Usher, Michael Saunders, Denis Peet, Robert Dobson, Andrew Adam, Paul Birks, H. J. B. Gustafsson, Lena McNeely, Jeff Paine, R .T. Richardson, David Wilson. Cambridge, United Kingdom: Cambridge University Press. www.cambridge.org/9780521765138.